One of the goals of modern cancer research is to decompose the oncogenic state of individual tumors directly in terms of cellular pathways that are aberrantly activated or deregulated. Despite large-scale efforts to systematically map the cancer genome, determining how alterations present within a given tumor interact to induce activated cellular states represents a major unmet challenge. The use of expression-based signatures has been effective in terms of improving classification of tumor samples according to sub-types, prognostic groups, or drug response. However, several significant limitations and challenges remain in order to make signature-based characterization effective and systematic enough to profile large and diverse collections of individual human tumors. For oncogenic signatures these limitations specifically include, but are not limited to, i) the uneven quality of experimental signatures from the literature, ii) the lack of validation in independent datasets, iii) the lack of transparency and annotation of the signatures, iv) the lack of specificity with respect to the genetic lesion they represent, and v) the limited understanding of their universality, tissue specificity and relevance to an in vivo context.
Thus a need exists for the identification of expression based signatures that are capable of classifying tumors.